The disclosure relates to “active medical devices” as may be defined by Directive 93/42/EC of 14 Jun. 1993 the Council of the European Communities. This disclosure particularly relates to implantable pacing, resynchronization and/or defibrillation systems and methods for use in the diagnosis and treatment of cardiac arrhythmias.
The disclosure may relate to active devices which may be implanted or devices that conduct analytic processing for purely or primarily diagnostic purposes—e.g. external systems for home monitoring of patients (clinical evaluation, home monitoring or remote monitoring). Such devices may, for example, form a wireless connection to an interrogation device disposed near the wearer of the device. The wireless connection may be activated at regular intervals, e.g. daily, to download and transmit the data collected by this device to a remote site station for analysis. This disclosure can also relate to the diagnosis of early cardiac decompensation events (events hereinafter referred to as “Adverse Events” or AE) using computerized methods for analysis of signals collected by the device.
The early detection of cardiac decompensation is a complex issue, reflected in a patient by multiple physiological parameters that may be evaluated and analyzed to optionally issue an alert. Some physiological parameters and symptoms associated with such cardiac decompensation may include:                The presence of fluid in the lungs, which may produce a decrease in respiratory amplitude and in the transthoracic impedance;        An acceleration of the respiration rate, in particular earlier during effort;        Weight gain and fatigue felt by the patient, leading the patient to exercise less, or to less intensely exercise, with a lower maximum heart rate;        A decrease in ejection fraction;        A decrease in the heart rate variability;        Changes in various morphological parameters of endocardial electrogram signals (EGM) and/or of endocardial acceleration signals (EA); and        The presence of episodes of atrial fibrillation (AF), especially conducted AF, which is also an important potential source of cardiac decompensation.        
It can be seen that one can thus evaluate a large amount of data related to the clinical condition of the patient. This data can be derived from the electrical activity of the myocardium, can include EGM signals collected by implantable electrodes, can include EA or cardiac bioimpedance signals, or can include signals reflecting changes in various parameters such as heart rate, ventilation amplitude and frequency, activity, etc. obtained from measurements by activity sensors (accelerometer G sensor) and/or physiological sensors (minute ventilation MV sensor). The history for this data can be determined, for example, daily. However, taken separately, analysis of daily indicators does not necessarily give good results.
Some combinations of these indicators can provide better analytical results. Numerous indexes can be developed for this purpose. EP 1867360 A2 (assigned to Sorin CRM S.A.S, previously known as ELA Medical) teaches crossing information from MV and G sensors with an endocardial acceleration or a cardiac bioimpedance signal. An algorithm creates a risk index of cardiac decompensation. The system generates a preventive warning composite indicator, relative to different levels, depending on the indexes produced by the algorithms.
The cardiac decompensation prevention algorithms used so far, however, can generate a relatively high number of false alarms. These are usually false positives, which are irrelevant to the doctor but may unnecessarily worry the patient. However, the consequences of repeated false alarms can be important when the device not only delivers an alert (diagnostic function), but also changes its operation to suit the supposed improvement or worsening of the patient's condition, e.g. by reprogramming some of the functions of the device or by automatic modification of triggering thresholds.
Settings of the different criteria for triggering the alert is usually adjusted by varying parameters (thresholds), incremental parameters (minimum or maximum percentage increase), or meta-rules used to analyze the evolution of the indexes and their combination over several days.
U.S. 2006/0010090 A1 describes an expert system comprising a plurality of thresholds corresponding to the various information collected by an implantable device. A practitioner can modify these thresholds, e.g., using information provided by the expert system to describe the history of the patient. It is thus possible to increase the selectivity of the system and avoid triggering an untimely alarm. This proposal however, does not take into consideration that the sensitivity and specificity (that is to say, the selectivity of the analysis) are generally regarded as two antagonist notions. In other words, with many known algorithms increased sensitivity is usually accompanied by a lower specificity—with correspondingly an increased risk of false alarms (“false positives”). Conversely, if the warning criteria are more stringent, cases of false positives are reduced, but with the risk of not triggering an alert in critical cases (“false negatives”), a situation that should be avoided as much as possible.
The object of the invention is to provide improved systems and methods for adapting an algorithm that prevents cardiac decompensation.